Dynamic redfish query uri binding from context oriented interaction

ABSTRACT

A system for data processing, comprising a first processor operating under algorithmic control and configured to receive audio data during a first session and to convert the audio data into encoded electrical data. A second processor operating under algorithmic control and configured to identify speech data in the encoded electrical data and to convert the speech data to text data. The second processor further configured to process the text data to identify one or more commands and one or more missing parameters of the commands. The second processor further configured to map context data to one or more commands and the one or more missing parameters and to select replacement parameter data corresponding to the missing parameter data from the mapped context data.

TECHNICAL FIELD

The present disclosure relates generally to data processing, and morespecifically to a system and method for dynamic Redfish query URIbinding from context oriented interaction.

BACKGROUND OF THE INVENTION

Voice command systems allow a user to interact with a data processingsystem using voice commands, but are not readily able to provide a userwith information about the expected parameters for the voice commands.As a result, complex commands are often unable to be implemented usingvoice commands.

SUMMARY OF THE INVENTION

A system for data processing is disclosed that includes a firstprocessor that receives audio data during a first session and convertsthe audio data into encoded electrical data. A second processoridentifies speech data in the encoded electrical data and converts thespeech data to text data, processes the text data to identify one ormore commands and one or more missing parameters of the commands, andmaps context data to one or more commands and the one or more missingparameters, to select replacement parameter data corresponding to themissing parameter data from the mapped context data.

Other systems, methods, features, and advantages of the presentdisclosure will be or become apparent to one with skill in the art uponexamination of the following drawings and detailed description. It isintended that all such additional systems, methods, features, andadvantages be included within this description, be within the scope ofthe present disclosure, and be protected by the accompanying claims.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Aspects of the disclosure can be better understood with reference to thefollowing drawings. The components in the drawings may be to scale, butemphasis is placed upon clearly illustrating the principles of thepresent disclosure. Moreover, in the drawings, like reference numeralsdesignate corresponding parts throughout the several views, and inwhich:

FIG. 1 is a diagram of a system for dynamic Redfish query URI bindingfrom context-oriented interaction, in accordance with an exampleembodiment of the present disclosure;

FIG. 2 is an algorithm for dynamic query binding from context-orientedinteraction, in accordance with an example embodiment of the presentdisclosure;

FIG. 3 is an algorithm for dynamic Redfish query URI binding fromcontext-oriented interaction, in accordance with an example embodimentof the present disclosure; and

FIG. 4 is a diagram of a context pooling system, in accordance with anexample embodiment of the present disclosure.

DETAILED DESCRIPTION OF THE INVENTION

In the description that follows, like parts are marked throughout thespecification and drawings with the same reference numerals. The drawingfigures may be to scale and certain components can be shown ingeneralized or schematic form and identified by commercial designationsin the interest of clarity and conciseness.

An intelligent virtual assistant (IVA) or intelligent personal assistant(IPA) is a software agent that can perform tasks or services for anindividual based on commands or questions. Some virtual assistants areable to interpret human speech and respond using synthesized voices. Forexample, the Redfish uniform resource identifier (URI) binding can bestatically derived and mapped against such voice user interfacecommands. Once a session is established, the recursive interaction withcontext is not used to derive the Redfish URI binding dynamically.Examples of such voice commands and static generated Redfish URI isshown below:

Voice Command Static Generated Redfish URI Power on the server X/redfish/v1/Chassis/<Server X>/action/actions/PowerUp How is the healthof the /redfish/v1/Chassis/<Server server X X>/HealthStatus CPU healthof server X /redfish/v1/Chassis/<Server X>/HealthStatus/CPUHealthStatusWhat is the Processor Name redfish/v1/Chassis/<Server of the CPU 1 inserver X X>/Processors/CPU1/ProcessorName

In these existing methods, the user voice commands are processed andconverted into English language statements. Natural language processing(NLP) can then optionally be applied, and content can be prepared thatis statically matched with the associated Redfish URI. Since the voiceto text conversion and NLP is processed for the given command in thebelow example, the server X is repeated in all the voice commandswithout which the mapping of Redfish URI cannot be completed.

The present disclosure provides a system and method to allow the uservoice commands to be processed and converted into English languagestatements. Additionally, the session oriented context can be built overthe series of voice commands. In this manner, session based content canbe prepared dynamically, and an associated Redfish URI can be mappedusing the Redfish Query parameters.

For example, in the previous method, the text server X is repeated inall the voice commands. But with the proposed system and method, theserver X is derived from the session content and the Redfish URI can beformed dynamically, including any query parameters, such as select,filter, expand and so forth. Examples of the present disclosure areshown below, with changes indicated in underlining and bold:

Interactive Voice Context oriented Dynamic Command Generated URI Poweron the server X /redfish/v1/Chassis/<Server X>/action/actions/PowerUpHow is the health  

/redfish/v1/Chassis/<Server

X>/HealthStatus CPU health of /redfish/v1/Chassis/<Server

X>/HealthStatus? $select=CPUHealthStatus What is the Processorredfish/v1/Chassis/<Server Name X>/Processors/CPU?

$select=ProcessorName

Show critical components /redfish/v1/Chassis/<Server X>/HealthStatus?$filter=Status   eq 'Critical '

In these examples, the Redfish select parameter can be used to indicateto the implementation that it should return a subset of the propertiesof the resource based on the value of the select clause. The Redfishfilter parameter can be used to indicate to the implementation that itshould include a subset of the members of a collection based on theexpression specified as the value of the filter clause.

In the present disclosure, a remote access controller, such as the DelliDRAC remote access controller or other suitable remote accesscontrollers, can be used to monitor incoming voice commands, to keeptrack of the sequence of commands and to build the context. Whensubsequent command instructions are not given with the requiredargument, the present disclosure can derive the appropriate argumentfrom the context. Similarly, from the context semantic analysis, anappropriate Redfish query parameter can be chosen. Then, along with thederived arguments associated with query parameter, a dynamic Redfish URIcan be framed to match the voice command. The dynamic Redfish URI can begiven as a request to the Redfish daemon, and the JavaScript ObjectNotation (JSON) output response can be parsed and converted to a voiceresponse using a text to voice converter, to allow a voice response tobe passed to the user. The interaction and the context buildingcontinues and a more accurate Redfish URI can be formed.

Currently, there is no solution which provides a sessioncontext-oriented redfish URI mapping. In the present disclosure, Redfishquery processing can be dynamically selected, as a function ofsession-based intent, and mapped based on the user interactions. Forexample, based on a user acknowledgement, a recursive Redfish URI queryexpansion can be performed to match the intention of the user.

The present disclosure includes a session manager module that can beimplemented as one or more algorithmic instruction sets that are used tocontrol a processor or other suitable logic devices that can beconfigured to manage a number of active sessions, such as a maximumnumber of allowed sessions. The session manager can also be used tomanage the associated contexts for a session, such as the incoming IPaddress of a device, a geographical region associated with a device,device bandwidth, device history, recent commands received from adevice, statistics about the past device usage and so forth.

The present disclosure can also include an intelligent authenticationservice module that can be implemented as one or more algorithmicinstruction sets that are used to control a processor or other suitablelogic devices to authenticate a user. The intelligent authenticationservice module can interact with an existing authentication service,such as a password based service, an OAUTH open standard for accessdelegation, by deriving an active child session or in other suitablemanners. The intelligent authentication service module can present auser with multiple queries, such as personal, organizational,geographical, server hardware specific and so forth, and can registersthe responses against the user. In another example embodiment, it cantrain a voice recognition module to assist with voice authentication. Inyet another example embodiment, when a user requests information basedon a configured level of criticality (such as a Redfish GET, or aRedfish—POST/PATCH to perform an action), the intelligent authenticationmodule can select random questions and validate the user, such as byasking more questions for a POST URI than a GET URI or in other suitablemanners. If a user response is suspicious against the answers, theintelligent authentication module can build dynamic questions likespecific actions performed in the recent past (such as a date and timeof a last login or a name of a virtual disk created), specificinformation about the server (such as a server location or a firmwareversion) or other suitable questions.

The present disclosure can also utilize an intelligent context to aRedfish URI mapper module that can be implemented as one or morealgorithmic instruction sets that are used to control a processor orother suitable logic devices that are configured to dynamically generatea Redfish URI. This module can be configured to internally maintain alist of available Redfish URIs and a required number of arguments forthe URI, such that whenever a user provides only partial information, itcan invoke a context manager to provide session details, history,statistics of the user and the type, a nature of the required argumentsand other suitable data, along with the provided partial information.The module can collect the top rated high ranked associated words from acontext manager and map them according to the order of the arguments.Based on the context, this module can be configured to predicts a userintention and to build recursive Redfish URI query parameters ($SELECT,$FILTER, $EXPAND), to aid with obtaining an intended response.

The present disclosure can also utilize a Redfish URI Manager that canbe implemented as one or more algorithmic instruction sets that are usedto control a processor or other suitable logic devices that areconfigured to execute a URI query. This module can get a JSON responsefrom a Redfish PULL service and pass the response to convert the text tovoice and send it to the user. If the URI query is related to streamingof the data and notifications, then this module can register orsubscribe the requested data from the PUSH service (Server SideEventing) and can convert the JSON response to voice, to send the steamof data and notification to the user over the network.

FIG. 1 is a diagram of a system 100 for dynamic Redfish query URIbinding from context-oriented interaction, in accordance with an exampleembodiment of the present disclosure. System 100 includes voice commanddevice 102 which is connected over network 124 to remote accesscontroller 126, which can include session manager 104, authenticationservice 106, session context 108, global server context 110, RESTresponse to voice converter 112, Redfish URI manager 114, context toRedfish URI mapper 116, REST push service 118 REST pull service 120 andRedfish URI database 122, and can be implemented in hardware or asuitable combination of hardware and software.

Voice command device 102 can be implemented as one or more audio devicesthat can receive audio signals and process the audio signals to generateencoded electrical signals that represent the audio signals, one or moreaudio devices that can receive encoded electrical signals and generateaudio signals, and one or more processors that operate under control ofone or more algorithms that cause the processors to receive the encodedelectrical signals and to transmit the encoded electrical signals overnetwork 124 to session manager 104, and to receive encoded electricalsignals from session manager 104 to process the received encodedelectrical signals to generate audio signals. In different exampleembodiments, voice command device 102 can be implemented as a personalcomputer, a cellular telephone, a television set, a remote control for atelevision set, an artificial intelligence interactive system (e.g. a“chatbot”), an appliance or other suitable consumer devices.

Session manager 104 can be implemented as one or more algorithms thatare loaded onto a processor and that are configured to cause theprocessor to manage a data communications session between voice commanddevice 102 and authentication service 104, REST response to voiceconverter 112 and other suitable systems and components of system 100.In one example embodiment, session manager 104 can establish a datacommunications session with voice command device 102 over network 124,such as by exchanging Internet protocol messages and address data, toallow voice command device to interact with the other components ofsystem 100, such as through a remote access controller such as the DelliDRAC system available from Dell of Austin, Tex. or other suitableremote access controllers.

Authentication service 106 can be implemented as one or more algorithmsthat are loaded onto a processor and that are configured to cause theprocessor to authenticate voice command device, such as to ensure thatdata received from a third party is authentic and not from a hostilethird party. In one example embodiment, authentication service 106 canbe coupled to session manager 104 and session context 108 over a systemdata bus, one or more logical devices or in other suitable manners, asshown, as well as to other suitable systems and components.Authentication service 106 can interact with conventional authenticationservices, such as password-based or OAUTH services, and can determinerequired parameters for an active communications session. Authenticationservice 106 can also be configured to generate queries that can be usedto determine personal identification of a user, organizationalaffiliations of a user, a geographical location of a user or device,server hardware specific data or other suitable data, and can registerthe responses against the user. Authentication service 106 can alsoimplement training of a local or remote voice recognition module toassist with voice authentication, such as by generating data andcontrols that cause the voice recognition module to store voice profiledata after a user has been authenticated by authentication service 106,to allow authentication service 106 to authenticate a user if they havepreviously been authenticated by authentication service 106 and havebeen recognized by a voice recognition module, or in other suitablemanners.

In one example embodiment, a user instruction for information, such as aRedfish—GET command, or to perform an action, such as a Redfish POST orPATCH command, that is based on a configured level of criticality cancause authentication service 106 to validate or re-validate the user. Inthis example embodiment, authentication service 106 can ask generatemore queries for a POST URI than for a GET URI, or other suitableauthentication protocols can be implemented. If a user response isinconsistent with previous responses obtained from the user,authentication service 106 can generate additional dynamically-selectedquestions, such as queries based on specific actions performed in recentpast (such as the last login date or time or the name of a virtual diskthat was created), queries based on specific information about theserver (such as a server physical location or a firmware version), orother suitable queries.

Session context 108 can be implemented as one or more algorithms thatare loaded onto a processor and that are configured to cause theprocessor to generate logical association between text from differentusers, different sessions, different applications and so forth. In oneexample embodiment, session context 108 can be coupled to authenticationservice 106, global server context 110, context to Redfish URI mapper116 and other suitable systems or devices over a system data bus, one ormore logical devices or in other suitable manners, as shown, as well asto other suitable systems and components. In another example embodiment,a user can perform associated tasks during a session, can use textstrings in association with those tasks with an associated frequency,and can otherwise generate text strings when interacting with system 100that can be used to determine context associated with translated speechcommands. Session context can also be configured to use context datagenerated in different domains to improve the uniformity of context thatis derived from text strings. In one example embodiment, a task can beassociated with specific types of equipment, and the text stringsgenerated by users when interacting with system 100 to control thatequipment can be used to generate logical associations within the textstrings as well as with data associated with the equipment. As such, thefollowing logical associations can be identified both within a domain orbetween unrelated domains, depending on the associated task, location,session, equipment and application:

One Level Examples

-   -   text to text (e.g a user first says “pause” then says “how are        you,” no action in response to second statement is determined        from context).    -   text to task (e.g. a user first says “start compiling program A”        then says “run,” running program A is determined from context).    -   text to location (e.g. a user says “I am in the office” and then        says “print,” using a printer in the office is determined from        context).    -   text to session (e.g. a user says “order pencils” and order is        placed in session with equipment provider based on context).    -   text to equipment (e.g. a user says “turn up volume” and volume        control for audio equipment is determined from context).    -   text to application (e.g. a user says “pause” while using        dictation application and pausing the dictation application is        determined from context).

Two Level Example

-   -   text to text to task (e.g a user is working on program A and        says “pause” then says “how are you,” no action in response to        second statement is determined from context, user then says        “start compiling program A,” compiling program A is determined        from context).

Three Level Example

-   -   text to text to task to location (e.g a user is working on        program A and says “pause” then says “how are you,” no action in        response to second statement is determined from context, user        then says “start compiling program A,” compiling program A on        local compiler is determined from context).

As can be seen, the logical relationships that can be identified can bebased on a suitable combination of the different variables, such thatsystem 100 can significantly improve the ability to determine contextfor verbal commands. In this manner, a user who is unfamiliar with atask, location, session, equipment, application or other aspect ofsystem 100 can be queried to provide additional information whereneeded, or additional information can be determined from context,depending on the situation.

Global server context 110 can be implemented as one or more algorithmsthat are loaded onto a processor and that are configured to cause theprocessor to obtain context data from one or more different domains,where each domain contains context information having the same taxonomyas the context information of the current domain. In one exampleembodiment, global server context 110 can be coupled to session context108, context to Redfish URI mapper 116 and other suitable systems ordevices over a system data bus, one or more logical devices or in othersuitable manners, as shown, as well as to other suitable systems andcomponents. In another example embodiment, context data from differentdomains can be added to improve the ability of system 100 to determinecontext, where each domain can be selected to determine whether theadded context data is effective. Context database versions can be usedto roll back the context database to an earlier version, if a problemwith the integration is detected.

REST response to voice converter 112 can be implemented as one or morealgorithms that are loaded onto a processor and that are configured tocause the processor to generate a response to a voice converter usingrepresentational state transfer (REST). In one example embodiment, RESTresponse to voice converter 112 can be coupled to session manager 104,Redfish URI manager 114, REST push service 118 and other suitablesystems or devices over a system data bus, one or more logical devicesor in other suitable manners, as shown, as well as to other suitablesystems and components. REST response to voice converter 112 can usecontext data provided by system 100 in generating responses to voiceconverters.

Redfish URI manager 114 can be implemented as one or more algorithmsthat are loaded onto a processor and that are configured to cause theprocessor to interact with context to Redfish URI mapper 116 to generateRedfish uniform resource identifiers. In one example embodiment, RedfishURI manager 114 can be coupled to REST response to voice converter 112,context to Redfish URI mapper 116, REST push service 118, REST pullservice 120 and other suitable systems or devices over a system databus, one or more logical devices or in other suitable manners, as shown,as well as to other suitable systems and components.

Context to Redfish URI mapper 116 can be implemented as one or morealgorithms that are loaded onto a processor and that are configured tocause the processor to dynamically generate a Redfish URI. In oneexample embodiment, context to Redfish URI mapper 116 can be coupled toRedfish URI manager 114, Redfish URI database 122, context session 108,global server context 110 and other suitable systems or devices over asystem data bus, one or more logical devices or in other suitablemanners, as shown, as well as to other suitable systems and components.Context to Redfish URI mapper 116 can be configured to internallymaintain a list of available Redfish URIs and the required number ofarguments for the URI. Whenever a user provides only partial informationin a voice response, context to Redfish URI mapper 116 can be configuredto provide session details, history, statistics of the user and thetype, nature of the required arguments along with the given partialinformation. Context to Redfish URI mapper 116 can collect the top ratedhigh ranked associated words and map them according to the order of thearguments. Based on the context, context to Redfish URI mapper 116 canbe configured to select a most probable user intention and to build arecursive Redfish URI query parameter ($SELECT, $FILTER, $EXPAND) toconfirm a probable user response or to otherwise get the intendedresponse.

REST push service 118 can be implemented as one or more algorithms thatare loaded onto a processor and that are configured to cause theprocessor to provide a representational state transfer push service togenerate outgoing messages that are pushed to users, i.e. which are notgenerated in response to a user query. In one example embodiment, RESTpush service 118 can be coupled to Redfish URI manager 114, RESTresponse to voice converter 112 and other suitable systems or devicesover a system data bus, one or more logical devices or in other suitablemanners, as shown, as well as to other suitable systems and components.

REST pull service 120 can be implemented as one or more algorithms thatare loaded onto a processor and that are configured to cause theprocessor to provide a representational state transfer pull service togenerate outgoing messages that are pulled to users, i.e. which aregenerated in response to a user query. In one example embodiment, RESTpull service 120 can be coupled to Redfish URI manager 114 and othersuitable systems or devices over a system data bus, one or more logicaldevices or in other suitable manners, as shown, as well as to othersuitable systems and components.

Redfish URI database 122 can be implemented as one or more algorithmsthat are loaded onto a processor and that are configured to cause theprocessor to provide a Redfish URI dataset, such as to provide data inresponse to structured queries. In one example embodiment, Redfish URIdatabase 122 can be coupled to context to Redfish URI mapper 116 andother suitable systems or devices over a system data bus, one or morelogical devices or in other suitable manners, as shown, as well as toother suitable systems and components.

Network 124 can be implemented as a wireline network, a wirelessnetwork, an optical network, other suitable networks or a suitablecombination of networks, to allow voice command device 102 to interactwith other components of system 100.

FIG. 2 is an algorithm 200 for dynamic query binding fromcontext-oriented interaction, in accordance with an example embodimentof the present disclosure. Algorithm 200 can be implemented in hardwareor a suitable combination of hardware and software.

Algorithm 200 begins at 202, where a processor operates underalgorithmic control to allow it to receive a voice command from a user.In one example embodiment, the voice command can be converted from anaudio signal to a digital signal and processed to generate a textstring, such as by using speech recognition processing at a voicecommand device, at a remote access controller or in other suitablemanners. The algorithm then proceeds to 204.

At 204, a processor operates under algorithmic control to translate avoice command. In one example embodiment, the voice command can betranslated using an authentication server to first authenticate theperson providing the voice command, the system that has provided thevoice command or in other suitable manners. The voice command can thenbe translated using a suitable protocol, such as a Redfish protocol orin other suitable manners. The algorithm then proceeds to 206.

At 206, a processor operates under algorithmic control to determinestate data, session context and other suitable data associated with thevoice command. In one example embodiment, the voice command can beincomplete and can require additional data pertaining to the state ofthe system, the session context of the command or other suitable data.The algorithm then proceeds to 208.

At 208, a processor operates under algorithmic control to modify thecommand based on the state data, the session data or other suitabledata. In one example embodiment, the missing data associated with thevoice command can be obtained using one or more additional algorithmicprocesses from a database of context data associated with system states,sessions, equipment, applications, geographic locations and so forth.The algorithm then proceeds to 210.

At 210, a processor operates under algorithmic control to modify statedata associated with the voice command. In one example embodiment, thestate data can be associated with the voice command, and can be used tomodify the voice command to allow it to comply with a predeterminedprotocol, such as by completing any missing components of the command orin other suitable manners. The algorithm then proceeds to 212.

At 212, a processor operates under algorithmic control to process thecommand, such as after it has been suitable modified or in othersuitable manners. In one example embodiment, the processor can generateadditional text to speech data for transmission to the voice commanddevice 102, as well as other suitable functions.

In operation, algorithm 200 provides for dynamic query binding fromcontext-oriented interaction. Although algorithm 200 is shown as aflowchart, a person of skill in the art will recognize thatobject-oriented programming, state diagrams, ladder diagrams or othersuitable logic structure or programming conventions can be used toimplement algorithm 200.

FIG. 3 is an algorithm 300 for dynamic Redfish query URI binding fromcontext-oriented interaction, in accordance with an example embodimentof the present disclosure. Algorithm 300 can be implemented in hardwareor a suitable combination of hardware and software.

Algorithm 300 begins at 302, where a processor operates underalgorithmic control to allow it to receive a voice command from a userat a remote controller. In one example embodiment, the voice command canbe converted from an audio signal to a digital signal and processed togenerate a text string, such as by using speech recognition processingat a voice command device, at a remote access controller or in othersuitable manners. The voice command can then be transmitted to theremote controller over a network by the processor, such as by engagingin a session with the remote controller, by initiating the session withthe voice command or in other suitable manners. The algorithm thenproceeds to 304.

At 304, a processor operates under algorithmic control to translate avoice command. In one example embodiment, the voice command can betranslated using an authentication server to first authenticate theperson providing the voice command, the system that has provided thevoice command or in other suitable manners. The voice command can thenbe translated using a suitable protocol, such as a Redfish protocol orin other suitable manners. The algorithm then proceeds to 306.

At 306, a processor operates under algorithmic control to determine acommand sequence. In one example embodiment, the command sequence can bedetermined based on a command sequence protocol that is used by theprocessor, from state data stored in a data memory that is associatedwith the command sequence or in other suitable manners. The algorithmthen proceeds to 308.

At 308, a processor operates under algorithmic control to determinewhether the sequence is correct. If it is determined that the sequenceis correct, the algorithm proceeds to 310, otherwise the algorithmproceeds to 314.

At 310, a processor operates under algorithmic control to modify statedata associated with the voice command. In one example embodiment, thestate data can be associated with the voice command, and can be used tomodify the voice command to allow it to comply with a predeterminedprotocol, such as by completing any missing components of the command orin other suitable manners. The algorithm then proceeds to 312.

At 312, a processor operates under algorithmic control to convert aresponse to audio data. In one example embodiment, the conversion toaudio data can be performed at a remote access controller, at a voicecommand device or in other suitable manners.

At 314, a processor operates under algorithmic control to modify thecommand based on the state data, the session data or other suitabledata. In one example embodiment, the missing data associated with thevoice command can be obtained using one or more additional algorithmicprocesses from a database of context data associated with system states,sessions, equipment, applications, geographic locations and so forth.The algorithm then proceeds to 316.

At 316, a processor operates under algorithmic control to choose a queryparameter. In one example embodiment, the query parameter can beselected to obtain additional data that is required to comply with aprotocol, such as a Redfish URI or other suitable protocols. Thealgorithm then proceeds to 318.

At 318, a processor operates under algorithmic control to frame aRedfish URI to match the response to the query parameter. In one exampleembodiment, contextual data can be used to frame a Redfish URI to matchthe response to the query parameter, or other suitable processes canalso or alternatively be used. The algorithm then proceeds to 320.

At 320, a processor operating under processor control parses aJavaScript output notation response. In one example embodiment, theJavaScript output notation response can include human-readable text thatis used to provide an input to a text to speech converter, or othersuitable processes can also or alternatively be used. The algorithm thenproceeds to 312.

In operation, algorithm 300 provides for dynamic Redfish query URIbinding from context-oriented interaction. Although algorithm 300 isshown as a flowchart, a person of skill in the art will recognize thatobject-oriented programming, state diagrams, ladder diagrams or othersuitable logic structure or programming conventions can be used toimplement algorithm 300.

FIG. 4 is a diagram of a context pooling system 400, in accordance withan example embodiment of the present disclosure. Context pooling system400 includes missing parameter selection 402, context pooling 404,session 1, user 1, session 2, user 2, domain 1, domain 2 and textpattern matching 406, each of which can be implemented in hardware or asuitable combination of hardware and software.

Missing parameter selection 402 can be implemented as one or morealgorithms operating on a processor that cause the processor to identifya missing parameter associated with a voice command and to select datathat completes the missing parameter. In one example embodiment, missingparameter 402 can use context pooling to select the data, or othersuitable processes can also or alternatively be used.

Context pooling 404 can be implemented as one or more algorithmsoperating on a processor that cause the processor to obtain text anddata from a plurality of sessions and a plurality of domains and poolsthe text and data to determine contextually relevant data to complete avoice command, consistent with a voice command protocol. In one exampleembodiment, context pooling 404 can obtain text and data having apredetermined taxonomy from two or more domains, and can combine thattext and data to provide context pooling for selection of missingparameters for a voice command.

Session 1 and user 1, and session 2 and user 2 can each be a computersession having an associated user or other suitable functionality thatcan generate voice commands having one or more associated parameters. Inone example embodiment, session 1 can include local and remote accesssystems that provide user 1 with voice command functionality, where user1 can provide voice commands that do not include all relevantparameters. In order to complete the voice command, system 400 canutilize context pooling to determine the correct parameter, based on theparameter provided by other users for the same context in the same orother domains. Domain 1 and domain 2 can each include a plurality ofsessions and associated users, where the sessions are used to providevoice commands that include one or more parameters that are provided bythe users.

Text pattern matching 406 can be implemented as one or more algorithmsoperating on a processor that cause the processor to match text and datafrom different domains to facilitate pooling of context pooling. In oneexample embodiment, each domain can use a common taxonomy that allowsthe text pattern matching to be performed on relevant fields, or othersuitable processes can also or alternatively be used.

As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof. As used herein, the term “and/or”includes any and all combinations of one or more of the associatedlisted items. As used herein, phrases such as “between X and Y” and“between about X and Y” should be interpreted to include X and Y. Asused herein, phrases such as “between about X and Y” mean “between aboutX and about Y.” As used herein, phrases such as “from about X to Y” mean“from about X to about Y.”

As used herein, “hardware” can include a combination of discretecomponents, an integrated circuit, an application-specific integratedcircuit, a field programmable gate array, or other suitable hardware. Asused herein, “software” can include one or more objects, agents,threads, lines of code, subroutines, separate software applications, twoor more lines of code or other suitable software structures operating intwo or more software applications, on one or more processors (where aprocessor includes one or more microcomputers or other suitable dataprocessing units, memory devices, input-output devices, displays, datainput devices such as a keyboard or a mouse, peripherals such asprinters and speakers, associated drivers, control cards, power sources,network devices, docking station devices, or other suitable devicesoperating under control of software systems in conjunction with theprocessor or other devices), or other suitable software structures. Inone exemplary embodiment, software can include one or more lines of codeor other suitable software structures operating in a general purposesoftware application, such as an operating system, and one or more linesof code or other suitable software structures operating in a specificpurpose software application. As used herein, the term “couple” and itscognate terms, such as “couples” and “coupled,” can include a physicalconnection (such as a copper conductor), a virtual connection (such asthrough randomly assigned memory locations of a data memory device), alogical connection (such as through logical gates of a semiconductingdevice), other suitable connections, or a suitable combination of suchconnections. The term “data” can refer to a suitable structure forusing, conveying or storing data, such as a data field, a data buffer, adata message having the data value and sender/receiver address data, acontrol message having the data value and one or more operators thatcause the receiving system or component to perform a function using thedata, or other suitable hardware or software components for theelectronic processing of data.

In general, a software system is a system that operates on a processorto perform predetermined functions in response to predetermined datafields. A software system is typically created as an algorithmic sourcecode by a human programmer, and the source code algorithm is thencompiled into a machine language algorithm with the source codealgorithm functions, and linked to the specific input/output devices,dynamic link libraries and other specific hardware and softwarecomponents of a processor, which converts the processor from a generalpurpose processor into a specific purpose processor. This well-knownprocess for implementing an algorithm using a processor should requireno explanation for one of even rudimentary skill in the art. Forexample, a system can be defined by the function it performs and thedata fields that it performs the function on. As used herein, a NAMEsystem, where NAME is typically the name of the general function that isperformed by the system, refers to a software system that is configuredto operate on a processor and to perform the disclosed function on thedisclosed data fields. A system can receive one or more data inputs,such as data fields, user-entered data, control data in response to auser prompt or other suitable data, and can determine an action to takebased on an algorithm, such as to proceed to a next algorithmic step ifdata is received, to repeat a prompt if data is not received, to performa mathematical operation on two data fields, to sort or display datafields or to perform other suitable well-known algorithmic functions.Unless a specific algorithm is disclosed, then any suitable algorithmthat would be known to one of skill in the art for performing thefunction using the associated data fields is contemplated as fallingwithin the scope of the disclosure. For example, a message system thatgenerates a message that includes a sender address field, a recipientaddress field and a message field would encompass software operating ona processor that can obtain the sender address field, recipient addressfield and message field from a suitable system or device of theprocessor, such as a buffer device or buffer system, can assemble thesender address field, recipient address field and message field into asuitable electronic message format (such as an electronic mail message,a TCP/IP message or any other suitable message format that has a senderaddress field, a recipient address field and message field), and cantransmit the electronic message using electronic messaging systems anddevices of the processor over a communications medium, such as anetwork. One of ordinary skill in the art would be able to provide thespecific coding for a specific application based on the foregoingdisclosure, which is intended to set forth exemplary embodiments of thepresent disclosure, and not to provide a tutorial for someone havingless than ordinary skill in the art, such as someone who is unfamiliarwith programming or processors in a suitable programming language. Aspecific algorithm for performing a function can be provided in a flowchart form or in other suitable formats, where the data fields andassociated functions can be set forth in an exemplary order ofoperations, where the order can be rearranged as suitable and is notintended to be limiting unless explicitly stated to be limiting.

It should be emphasized that the above-described embodiments are merelyexamples of possible implementations. Many variations and modificationsmay be made to the above-described embodiments without departing fromthe principles of the present disclosure. All such modifications andvariations are intended to be included herein within the scope of thisdisclosure and protected by the following claims.

What is claimed is: 1-18. (canceled)
 19. A system for data processing,comprising: a first processor operating under algorithmic control andconfigured to receive audio data during a first session and to convertthe audio data into encoded electrical data; a second processorconfigured to process the encoded electrical data to identify one ormore commands and one or more missing parameters of the commands; andthe second processor further configured to map context data to one ormore commands and the one or more missing parameters and to selectreplacement parameter data corresponding to the missing parameter datafrom the mapped context data.
 20. The system of claim 19 wherein theencoded electrical data is speech data.
 21. The system of claim 19wherein the encoded electrical data is text data.
 22. The system ofclaim 19 wherein the second processor comprises an authenticationservice configured to receive user data and to authenticate the userdata, wherein the missing parameters are used to authenticate the userdata.
 23. The system of claim 19 wherein the second processor comprisesa session context system configured to provide first session data to oneor more predetermined fields of the mapped context data.
 24. The systemof claim 19 wherein the second processor comprises a global servercontext system configured to provide data from one or more secondsessions to one or more predetermined fields of the mapped context data.25. The system of claim 19 wherein the second processor comprises acontext to Redfish URI mapper configured to assign the mapped contextdata in accordance with a Redfish protocol.
 26. The system of claim 25wherein the second processor comprises a Redfish URI manager configuredto implement the Redfish protocol.
 27. The system of claim 25 whereinthe second processor comprises a Redfish URI database configured toprovide Redfish URI data associated with the Redfish protocol.
 28. Thesystem of claim 21 wherein the second processor comprises arepresentational state transfer response to voice converter configuredto provide response data to a voice command.
 29. The system of claim 21wherein the second processor comprises a representational state transferpush service configured to provide response data that is unassociated tothe audio data.
 30. A method for data processing, comprising: receivingaudio data at a first processor operating under algorithmic controlduring a first session; processing the audio data to identify one ormore commands and one or more missing parameters of the commands using asecond processor; mapping context data to one or more commands and theone or more missing parameters using the second processor; and selectingreplacement parameter data corresponding to the missing parameter datafrom the mapped context data using the second processor.
 31. The methodof claim 30 further comprising: receiving user data; and authenticatingthe user data, wherein the missing parameters are used to authenticatethe user data.
 32. The method of claim 30 further comprising providingfirst session data to one or more predetermined fields of the mappedcontext data.
 33. The method of claim 30 further comprising providingdata from one or more second sessions to one or more predeterminedfields of the mapped context data.
 34. The method of claim 30 furthercomprising assigning the mapped context data in accordance with aRedfish protocol.
 35. The method of claim 34 wherein a Redfish URImanager is configured to implement the Redfish protocol.
 36. The methodof claim 34 wherein a Redfish URI database is configured to provideRedfish URI data associated with the Redfish protocol.
 37. The method ofclaim 30 further comprising providing response data to a voice commandusing a representational state transfer response.
 38. The method ofclaim 30 further comprising providing response data that is unassociatedwith the audio data using a representational state transfer pushservice.